Systems and methods for generating real time events

ABSTRACT

Events generation systems and methods are provided. The system obtains, in real time, input stream data from one or more sources, filters, the input stream data by identifying one or more authentic sources to obtain filtered input stream data, parses, the filtered input stream data to obtain validated data in a specific data format, performs apply, in real time, an analysis on the validated data and on the corresponding one or more authentic sources by applying, at least one of one or more metadata driven logics and one or more predefined rules and further generates generate, in real time, one or more real time events based on the analysis.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:India Application No. 201621010040, filed on Mar. 22, 2016. The entirecontents of the aforementioned application are incorporated herein byreference.

TECHNICAL FIELD

This disclosure relates generally to data processing techniques, andmore particularly to systems and methods for generating real time eventsby analyzing input stream data.

BACKGROUND

Data processing is of utmost importance for most of entities such asorganizations. Challenges remain in processing large number of data onBig Data platforms. For instance, one of the challenges typically facedby such organizations involves a scenario wherein input data is receivedfrom multiple sources. Data is captured from monitoring tools, and isreceived in multiple formats. Further, the challenge is in convertingthe received data into a standard format. Furthermore, such informationare obtained from data sources and may be incomplete and contain partialinformation of the system components and dependencies spread across thesystem. It is therefore difficult to determine and validate the sources,and as well as the data. Existing techniques do not process/execute datain real time. Moreover, such existing technique fail to integrate withbig data platform and are inefficient in complex events processing basedon the input data.

SUMMARY

The following presents a simplified summary of some embodiments of thedisclosure in order to provide a basic understanding of the embodiments.This summary is not an extensive overview of the embodiments. It is notintended to identify key/critical elements of the embodiments or todelineate the scope of the embodiments. Its sole purpose is to presentsome embodiments in a simplified form as a prelude to the more detaileddescription that is presented below. In view of the foregoing, anembodiment herein provides systems and methods for generating real timeevents.

In one embodiment, a processor implemented method is provided. Themethod comprising obtaining, in real time, input stream data from one ormore sources; filtering, in the real time, the input stream data byidentifying one or more authentic sources from the one or more sourcesto obtain filtered input stream data; parsing, in the real time, thefiltered input stream data such that the filtered input stream data isvalidated to obtain validated data in a specific data format; applying,in the real time, at least one of one or more metadata driven logics andone or more predefined rules on the validated data in the specific dataformat and the one or more authentic sources; performing, in the realtime, an analysis on the validated data in the specific data format andon corresponding the one or more authentic sources; and generating, inthe real time, one or more real time events based on the analysis. Inone embodiment, the step of generating one or more real time eventscomprises performing a comparison of a specific timestamp from the inputstream data with a predetermined threshold defined in the one or morepredefined rules. In another embodiment, the step of generating one ormore real time events comprises performing a comparison of a specificcount from the input stream data with a predetermined threshold definedin the one or more predefined rules. In an embodiment, the method mayfurther comprise communicating a notification message comprising acommand to the one or more authentic sources based on the analysis.

In another embodiment, a system is provided. The system comprising amemory storing instructions; one or more communication interfaces; oneor more hardware processors coupled to the memory using the one or morecommunication interfaces, wherein the one or more hardware processorsare configured by the instructions to obtain, in real time, input streamdata from one or more sources, filter, in the real time, the inputstream data by identifying one or more authentic sources from the one ormore sources to obtain filtered input stream data, parse, in the realtime, the filtered input stream data such that the filtered input streamdata is validated to obtain validated data in a specific data format,apply, in the real time, at least one of one or more metadata drivenlogics and one or more predefined rules on validated data in thespecific data format and the one or more authentic sources, perform, inthe real time, an analysis on the validated data in the specific dataformat and on corresponding the one or more authentic sources, andgenerate, in the real time, one or more real time events based on theanalysis. In an embodiment, the one or more real time events may begenerated by performing a comparison of a specific timestamp from theinput stream data with a predetermined threshold defined in the one ormore predefined rules. In another embodiment, the one or more real timeevents may be generated by performing a comparison of a specific countfrom the input stream data with a predetermined threshold defined in theone or more predefined rules. The one or more hardware processors may befurther configured by the instructions to communicate a notificationmessage comprising a command to the one or more authentic sources basedon the analysis.

In yet another embodiment, one or more non-transitory machine readableinformation storage mediums comprising one or more instructions isprovided. The one or more instructions which when executed by one ormore hardware processors causes obtaining, in real time, input streamdata from one or more sources; filtering, in the real time, the inputstream data by identifying one or more authentic sources from the one ormore sources to obtain filtered input stream data; parsing, in the realtime, the filtered input stream data such that the filtered input streamdata is validated to obtain validated data in a specific data format;applying, in the real time, at least one of one or more metadata drivenlogics and one or more predefined rules on the validated data in thespecific data format and the one or more authentic sources; performing,in the real time, an analysis on the validated data in the specific dataformat and on corresponding the one or more authentic sources; andgenerating, in the real time, one or more real time events based on theanalysis. In one embodiment, the step of generating one or more realtime events comprises performing a comparison of a specific timestampfrom the input stream data with a predetermined threshold defined in theone or more predefined rules. In another embodiment, the step ofgenerating one or more real time events comprises performing acomparison of a specific count from the input stream data with apredetermined threshold defined in the one or more predefined rules. Inan embodiment, the one or more instructions which when executed by oneor more hardware processors further causes communicating a notificationmessage comprising a command to the one or more authentic sources basedon the analysis.

It should be appreciated by those skilled in the art that any blockdiagram herein represent conceptual views of illustrative systemsembodying the principles of the present subject matter. Similarly, itwill be appreciated that any flow charts, flow diagrams, statetransition diagrams, pseudo code, and the like represent variousprocesses which may be substantially represented in computer readablemedium and so executed by a computing device or processor, whether ornot such computing device or processor is explicitly shown.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 is a block diagram of an events generation system according to anembodiment of the present disclosure;

FIG. 2 is a flow diagram illustrating a processor implemented method forgenerating real time events using the event generation system of FIG. 1according to an embodiment of the present disclosure;

FIG. 3 is a representation of one or more events generated by the eventsgeneration system of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 4 is a representation of one or more events and notificationmessages communicated by the events generation system of FIG. 1according to an embodiment of the present disclosure; and

FIG. 5 is an example of a metadata structure that includes one or moreprocesses according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

The embodiments herein and the various features and advantageous detailsthereof are explained more fully with reference to the non-limitingembodiments that are illustrated in the accompanying drawings anddetailed in the following description. The examples used herein areintended merely to facilitate an understanding of ways in which theembodiments herein may be practiced and to further enable those of skillin the art to practice the embodiments herein. Accordingly, the examplesshould not be construed as limiting the scope of the embodiments herein.

The words “comprising,” “having,” “containing,” and “including,” andother forms thereof, are intended to be equivalent in meaning and beopen ended in that an item or items following any one of these words isnot meant to be an exhaustive listing of such item or items, or meant tobe limited to only the listed item or items.

It must also be noted that as used herein and in the appended claims,the singular forms “a,” “an,” and “the” include plural references unlessthe context clearly dictates otherwise. Although any systems and methodssimilar or equivalent to those described herein can be used in thepractice or testing of embodiments of the present disclosure, thepreferred, systems and methods are now described.

Some embodiments of this disclosure, illustrating all its features, willnow be discussed in detail. The disclosed embodiments are merelyexemplary of the disclosure, which may be embodied in various forms.

Before setting forth the detailed explanation, it is noted that all ofthe discussion below, regardless of the particular implementation beingdescribed, is exemplary in nature, rather than limiting.

Referring now to the drawings, and more particularly to FIGS. 1 through5, where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

FIG. 1 is a block diagram of an events generation system 100 accordingto an embodiment of the present disclosure. The terms “event generationsystem” and “system” may be interchangeably used herein after. Thesystem 100 comprises a memory 102, a hardware processor 104, and aninput/output (I/O) interface 106. Although the exemplary block diagramand the associated description refers to a memory and a hardwareprocessor, it may be understood that one or more memory units and one ormore hardware processors may be comprised in the event generation system100. The memory 102 further includes one or more functional modules 108.The memory 102, the hardware processor 104, the input/output (I/O)interface 106, and/or the modules 108 may be coupled by a system bus ora similar mechanism. The event generation system 100 generates one ormore real time events for given input stream data received from (orobtained from) one or more sources. The one or more sources maycomprise, for example but are not limited to, Wi-Fi Routers, SCADASystems, Pressure Reducing Valves (PRV), Water Meters, GlobalPositioning Systems (GPS), sensors, and the like. The input stream datais parsed and filtered by the system 100 based on information receivedfrom the one or more sources (not shown in FIG. 1) through one or morenetworks (not shown in FIG. 1).

The memory 102, may store instructions, any number of pieces ofinformation, and data, used by a computer system, for example the system100 to implement the functions of the system 100. The memory 102 mayinclude for example, volatile memory and/or non-volatile memory.Examples of volatile memory may include, but are not limited to volatilerandom access memory (RAM). The non-volatile memory may additionally oralternatively comprise an electrically erasable programmable read onlymemory (EEPROM), flash memory, hard drive, or the like. Some examples ofthe volatile memory includes, but are not limited to, random accessmemory, dynamic random access memory, static random access memory, andthe like. Some example of the non-volatile memory includes, but are notlimited to, hard disks, magnetic tapes, optical disks, programmable readonly memory, erasable programmable read only memory, electricallyerasable programmable read only memory, flash memory, and the like. Thememory 102 may be configured to store information, data, instructions orthe like for enabling the system 100 to carry out various functions inaccordance with various example embodiments.

Additionally or alternatively, the memory 102 may be configured to storeinstructions which when executed by the hardware processor 104 causesthe system 100 to behave in a manner as described in variousembodiments. The memory 102 stores the functional modules andinformation, for example, information (e.g., time series data) receivedfrom the one or more sensors (not shown in FIG. 1) through the one ormore networks (not shown in FIG. 1).

The hardware processor 104 may be implemented as one or moremicroprocessors, microcomputers, microcontrollers, digital signalprocessors, central processing units, state machines, logic circuitries,and/or any devices that manipulate signals based on operationalinstructions. Further, the hardware processor 104 may comprise amulti-core architecture. Among other capabilities, the hardwareprocessor 104 is configured to fetch and execute computer-readableinstructions or modules stored in the memory 102. The hardware processor104 may include circuitry implementing, among others, audio and logicfunctions associated with the communication. For example, the hardwareprocessor 104 may include, but are not limited to, one or more digitalsignal processors (DSPs), one or more microprocessor, one or morespecial-purpose computer chips, one or more field-programmable gatearrays (FPGAs), one or more application-specific integrated circuits(ASICs), one or more computer(s), various analog to digital converters,digital to analog converters, and/or other support circuits.

The hardware processor 104 thus may also include the functionality toencode messages and/or data or information. The hardware processor 104may include, among others a clock, an arithmetic logic unit (ALU) andlogic gates configured to support operation of the hardware processor104. Further, the hardware processor 104 may include functionality toexecute one or more software programs, which may be stored in the memory102 or otherwise accessible to the hardware processor 104.

The modules 108 may comprise, for example, a process engine initializerthat is configured to initialize process engine associated beans (alsoreferred as “business functions”) by reading one or more processes frommetadata structure as shown in FIG. 5. The process engine initializer isfurther configured to cache the process flows (e.g., process metadatabeing cached). The modules 108 may further comprise a process enginemetadata implements the cached process flows and individual taskmetadata during a runtime execution. The modules 108 may furthercomprise a process engine executor that executes one or more processesby receiving real time events/feeds. For example, the process engineexecutor may receive metadata definition of process parallelism andunderline big data platform for process execution. Unlike conventionalsystems or methods where they fails to handle with multiple data formatsof inputs, the process engine executor obtains (or receives) one or moreinput stream data pertaining to one or more protocols (e.g., http,https, jmh, mqtt, and the like) in one or more formats for example, anExtensible Markup Language (XML) format, a JavaScript Object Notation(JSON) format, a Comma Separated Values (CSV) format, and the like.Furthermore, the process engine executor is configured to execute one ormore predefined rules from a rule base repository (e.g., DroolsWorkbench) stored in the memory 102. The module 108 may further comprisea process engine monitor that is configured to create one or moreproxies around one or more individual tasks to monitor one or moreprocess flows and corresponding statuses, and perform audit of data flowduring process execution.

FIG. 2, with reference to FIG. 1, is a flow diagram illustrating aprocessor implemented method for generating real time events using theevent generation system 100 according to an embodiment of the presentdisclosure. The steps of the method of the present disclosure will nowbe explained with reference to the components of the system 100 asdepicted in FIG. 1. The hardware processor 104 is configured by theinstructions stored in the memory 102. The hardware processor 104 whenconfigured by the instructions generates one or more events (e.g.,simple, medium, and/or complex) as described hereinafter. In anembodiment, at step 202, the hardware processor 104 obtains input streamdata (e.g., Wi-Fi data, Global Positioning System (GPS) data, data fromsupervisory control and data acquisition (SCADA) such as water flowrate, pressure rate, and the like) from one or more sources (e.g.,pressure sensor, heat sensor, and the like). Input stream data mayfurther include data coming from one or more intelligent personalassistants such as Speech Interpretation and Recognition Interface(Siri), and the like residing on one or more operating systemsassociated with computer systems and mobile communication devices. Instep 204, the hardware processor 104 filters the input stream data byidentifying one or more authentic sources from the one or more sourcesto obtain filtered input stream data. Sources that are registered(and/or subscribed to the events generation system 100 are identified asthe one or more authentic sources. The step of filtering the inputstream data also helps the events generation system to identifysource(s) that are not authentic, and tag that source under a blacklist.For example, sources transmitting input stream data with an unusualpattern can be identified as a source not registered to the eventgeneration system 100, and can be tagged under blacklist.

In step 206, the hardware processor 104 parses the filtered input streamdata such that the filtered input stream data is validated to obtainvalidated data in a specific data format. In step 208, the hardwareprocessor 104 applies at least one of one or more metadata driven logicsand one or more rules on (i) the validated data in the specific dataformat and (ii) one or more authentic sources. In one embodiment, theone or more metadata driven logics and one or more predefined rules areapplied on the one or more authentic sources due to the data nature. Theone or more metadata driven logics and the one or more predefined rulesare stored in the memory 102 and/or a cache (which may be integral partof the memory 102). In another embodiment, the events generation system100 may invoke the one or more metadata driven logics and one or morepredefined rules from an external repository stored on one or morecomputing devices or cloud environments. For instance, the one or moremetadata driven logics and one or more predefined rules may be invokedfrom the external repository through an external rule engine to outputevents/alerts in real time.

In step 210, the hardware processor 104 performs an analysis on thevalidated data in the specific data format and on corresponding the oneor more authentic sources. In an embodiment, the events generationsystem 100 performs analysis in real time on the validated date throughan in-memory processing using distributed caching mechanism (clustermode), where cache is also be distributed. In step 212, the hardwareprocessor 104 generates one or more real time events based on theanalysis. In one embodiment, an analysis such as a comparison of aspecific timestamp from the input stream data with a predeterminedthreshold defined in the one or more predefined rules may be performed.For example, assuming that input stream data comprises wait time ofpeople waiting at a bus stop to board a bus, which is a specifictimestamp, and the predefined threshold be 5 minutes. Assume the waittime is 10 minutes. The wait time is compared with the predeterminedthreshold. In this scenario, since the wait time is greater than thepredetermined threshold, the events generation system 100 generate anevent (e.g., an alert), and the same is communicated to a correspondingentity (e.g., a bus driver), or a travel agent. In another embodiment,an analysis such as a comparison of a specific count from the inputstream data with a predetermined threshold defined in the one or morerules may be performed. For example, assuming that the input streamcomprise count specific to people at a bus stop for boarding a bus, andthe predetermined threshold 10. Assume that the count is 20. The count(e.g., 20—number of people waiting in queue at a bus terminal) iscompared with the predetermined threshold (e.g., 10 maximum number ofpeople waiting in queue at a bus terminal as defined in the system 100).In this scenario, the count is greater than the predetermined threshold,the events generation system 100 may generate an event and the same iscommunicated to a corresponding entity (e.g., a bus driver), or a travelagent. This may enable to improve one or more services offered byservice providers (e.g., travel agents).

Further, the events generation system 100 enables the hardware processor104 to generate and communicate a notification message comprising acommand to the one or more authentic sources based on the analysis. Forexample, assuming that the input stream data comprises water flow ratefor different timestamps. The input stream data may further comprise thewater pressure. This information for example, water flow rate, and thewater pressure, may be analyzed by applying one or more metadata drivenlogics and/or one or more predefined rules on the water flow rate, andthe water pressure and corresponding events may be generated. Based onthe analysis, the events generation system 100 may communicate anotification message comprising a command to a water supply department.The notification message may indicate a certain portion of pipelinesupplying water that may be burst in the future. The notificationmessage may include a command to shut down corresponding valve and stopthe water supply, and get it rectified/replaced. The water supplydepartment may process this command providing appropriate instructionsto a control unit. The control unit upon receiving the command mayperform one or more actions. In an embodiment, one or more actions maycomprise, but are not limited to, shutting down operations of aparticular component that is likely to dysfunction.

FIG. 3, with reference to FIGS. 1-2, is a representation of one or moreevents generated by the events generation system 100 according to anembodiment of the present disclosure. Below is an illustrative exampleof input stream data (e.g., Wi-Fi data):

-   -   “Current Time”,“Client Ip Address”,“Client        MacAddress”,“Association Time”,“Vendor”,“Ap        Name”,“WifiName”,“Map Location”,“SSID”,“Profile”,“Session        Duration”,“Average Session ThroughPut”,“Ap MacAddress”,“Ap        Ip”,“Device Ip”,“Dissociation Time”    -   “20150813143000”,“127.0.0.1”,“a2:d2:e6:17:4b:29”,“20150813135100”,“vendor1”,“AP        Depot Shelter        23”,“wifi1”,“41.8799021054578,−87.6975253519201”,“ssid1”,“client        profile”,“0”,“153”,“0d:9e:12:34:d1:a1”,“127.0.0.1”,“127.0.0.1”,“0”    -   “20150813143000”,“127.0.0.1”,“a2:f8:84:f7:ec:23”,“20150813135100”,“vendor1”,“AP        Depot Shelter        23”,“wifi1”,“41.8799021054578,−87.6975253519201”,“ssid1”,“client        profile”,“0”,“172”,“0d:9e:12:34:d1:a1”,“127.0.0.1”,“127.0.0.1”,“0”    -   “20150813143000”,“127.0.0.1”,“a2:1b:56:ce:75:06”,“20150813135100”,“vendor1”,“AP        Depot Shelter        23”,“wifi1”,“41.8799021054578,−87.6975253519201”,“ssid1”,“client        profile”,“0”,“174”,“0d:9e:12:34:d1:a1”,“127.0.0.1”,“127.0.0.1”,“0”    -   “201508131430007127.0.0.1”,“a2:86:26:1c:0f:25”,“20150813135100”,“vendor1”,“AP        Depot Shelter        23”,“wifi1”,“41.8799021054578,−87.6975253519201”,“ssid1”,“client        profile”,“0”,“127”,“0d:9e:12:34:d1:a1”,“127.0.0.1”,“127.0.0.1”,“0”    -   “20150813143000”,“127.0.0.1”,“a2:cd:53:54:e4:13”,“20150813135100”,“vendor1”,“AP        Depot Shelter        23”,“wifi1”,“41.8799021054578,−87.6975253519201”,“ssid1”,“client        profile”,“0”,“181”,“0d:9e:12:34:d1:a1”,“127.0.0.1”,“127.0.0.1”,“0”

The above illustrative input stream data was filtered, parsed andvalidated. The validated data was then analyzed by applying one or moremetadata driven logics and/or one or more predefined rules to generateone or more events as depicted in FIG. 3. As depicted in FIG. 3, theevents are generated in terms of alerts, for example, 10 minutes isaverage wait time, and queue size is 5 people who are standing at a busstop to board a bus. The events may be delivered to one or more clientdevices (e.g., in this case travel agents) through one or morecommunication mediums for example, Big Data systems, File Systems, OLAPsystems, Email, Social networks, through message push protocols, and thelike.

FIG. 4, with reference to FIGS. 1-3, is a representation of one or moreevents and notification messages communicated by the events generationsystem 100 according to an embodiment of the present disclosure. FIG. 4illustrates prediction of pipe burst location by the events generationsystem 100. An illustrative example of the input stream data (e.g.,sensor data from a water supply department) is provided below:

-   -   “SNS102”,“SUBDMA10”,“20151118”,“14:00:00”,“66.5”,“F”,“698.42”,“Ips”,“350”,“MGa”    -   “SNS79”,“SUBDMA24”,“20151118”,“14:00:00”,“67.4”,“F”,“758.11”,“Ips”,“600”,“MGa”    -   “SNS83”,“SUBDMA29”,“20151118”,“14:00:00”,“67.4”,“F”,“680.7”,“Ips”,“400”,“MGa”    -   “SNS76”,“SUBDMA36”,“20151118”,“14:00:00”,“60.8”,“F”,“879.6”,“Ips”,“147.6”,“MGa”    -   “SNS111”,“SUBDMA37”,“20151118”,“14:00:00”,“61.8”,“F”,“831.24”,“Ips”,“100”,“MGa”    -   “SNS118”,“SUBDMA39”,“20151118”,“14:00:00”,“65”,“F”,“1011.23”,“Ips”,“780”,“MGa”    -   “SNS81”,“SUBDMA41”,“20151118”,“14:00:00”,“66.1”,“F”,“1033.81”,“Ips”,“9999”,“MG        a”    -   “SNS85”,“SUBDMA45”,“20151118”,“14:00:00”,“64.2”,“F”,“727.8”,“Ips”,“150”,“MGa”

SNS denotes an illustrative a unique identifier (e.g., MAC address) of aconnected computing device (e.g., a laptop, a mobile communicationdevice, and the like). SUBDMA is a network code (e.g., a location code)that corresponds to at least one source (e.g., a sensor device)providing the input stream data, and Ips (litre per second) and MGa(mega million gallons) are measurement units.

The above input stream data pertaining water flow rate (and/or waterpressure) and corresponding valves or distribution components arefiltered, parsed and validated. This input stream data is then analyzedto generate one or more events and further communicate a notificationmessage comprising a command to a corresponding source (e.g., a watersupply department). Based on the analysis, the events generation system100 has identified (or predicted) a pipe location that is likely toburst. The analysis may comprise for example, determining the pressureof water flow rate, and the strength of the portion of the pipe at thepipe location, and duration that the pipe can withstand before theburst, and the like. The pipe burst location is notified to the watersupply department so that appropriate actions are taken (e.g.,rectifying the pipe at the location, or shutting down the valvepertaining to the flow of water from the pipe that is likely to burst.

FIG. 5, with reference to FIGS. 1 through 4, is an example of a metadatastructure that includes one or more processes according to an embodimentof the present disclosure. The metadata structure may be stored in ametadata repository in the memory 102, in an example embodiment.

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

The embodiments of present disclosure herein addresses unresolvedproblem of processing input stream data to generate events for takingone or more controlled decisions. The embodiment, thus provides theevents generation system 100 that generates one or more events based onan analysis being performed on input stream data. Unlike conventionalsystems and methods which fail to (i) execute real time streams, and(ii) integrate with big data platforms, the events generation system 100processes in real time input stream data obtained from one or moresources, filters the input stream data in real time to identify andauthenticate sources and obtain one or more authentic sources. Theevents generation system 100 further validates the filtered input streamdata by parsing the filtered input stream data and converting thefiltered input stream data (received in one or more formats), to aspecific format that is easily understood, and can be interpreted by thesystem 100 (or any other systems coupled to the system 100) and obtainsvalidated data. This above functionalities may be achieved by the system100 using real time stream processors like apache spark and apachestorm, in an example embodiment. The events generation system 100further performs analysis on the validated data and the authenticsources by applying one or more predefined metadata driven logics andrules, based on which one or more events are generated. The events maybe in any form for example, alerts, commands, messages, email services,and the like. The read the data from real time stream processors, forexample, but are not limited to apache spark, apache storm, and thelike.

The embodiments of the present disclosure enable the events generationsystem 100 to execute the process over big data platform on real timemode using a relational database for example, apache phoenix. In otherwords, the events generation system 100 acts as a metadata basedorchestration system to handle real time Big Data real time streams andcreates one or more processes (e.g., business processes, system drivenprocesses) through metadata over a big data platform. The embodiments ofthe present disclosure further enable the events generation system 100to orchestrate real time systems for Wi-Fi, GPS and Sensor data, andproduces results in one or more messaging services, for example, but arenot limited to, Kafka Active MQ, and the like.

Additionally, the embodiments of the present disclosure enable theevents generation system 100 to asynchronously perform self-diagnosticson the system itself to determine (or check) for failure of input streamdata without hampering the overall performance of the system. Such checkmay comprise, for example, but is not limited to computing one or morestatistics related to a computer system such as determining availablememory for data processing, determining available power consumption,determining availability of one or more hardware processors, and othersystem components to perform one or more functionalities. This outputfrom the self-diagnostics enables the events generation system 100optimize the system capabilities for example, but are not limited toreducing memory space, increasing in processing data to a greater speed,and at the same time enabling reduction in power usage. Furthermore, theembodiments of the present disclosure enable the events generationsystem 100 to process real time events using one or more parallelprocessing mechanisms to enable quick response time (e.g., receive theevents from multiple sources) so that the system 100 can process thoseevents in parallel. Moreover, the ability to perform self-diagnostics bythe events generation system 100 enables a system (e.g., a computersystem) to identify one or more processes for example, but are notlimited to one or more business processes, one or more system drivenprocesses, and the like being executed in the computer system, that areeither malfunctioning, or consuming more power, more memory, moreprocessing time, and the like. Alternatively, the events generationsystem 100 may be an integral part of the computer system or externallyconnected to the computer system via one or more communicationinterfaces. The events generation system 100 enables rectification ofthe identified processes that are either malfunctioning, or consumingmore power, more memory, more processing time, and the like andaccordingly optimize system resources (e.g., memory optimization,increase in processing speed, power optimization by enabling powersaving mode).

It is, however to be understood that the scope of the protection isextended to such a program and in addition to a computer-readable meanshaving a message therein; such computer-readable storage means containprogram-code means for implementation of one or more steps of themethod, when the program runs on a server or mobile device or anysuitable programmable device. The hardware device can be any kind ofdevice which can be programmed including e.g. any kind of computer likea server or a personal computer, or the like, or any combinationthereof. The device may also include means which could be e.g. hardwaremeans like e.g. an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or a combination of hardware andsoftware means, e.g. an ASIC and an FPGA, or at least one microprocessorand at least one memory with software modules located therein. Thus, themeans can include both hardware means and software means. The methodembodiments described herein could be implemented in hardware andsoftware. The device may also include software means. Alternatively, theembodiments may be implemented on different hardware devices, e.g. usinga plurality of CPUs.

The embodiments herein can comprise hardware and software elements. Theembodiments that are implemented in software include but are not limitedto, firmware, resident software, microcode, etc. The functions performedby various modules described herein may be implemented in other modulesor combinations of other modules. For the purposes of this description,a computer-usable or computer readable medium can be any apparatus thatcan comprise, store, communicate, propagate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W), BLU-RAY, and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output (I/O) devices (including but not limited to keyboards,displays, pointing, devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modem and Ethernet cards are just a few of the currently availabletypes of network adapters.

A representative hardware environment for practicing the embodiments mayinclude a hardware configuration of an information handling/computersystem in accordance with the embodiments herein. The system hereincomprises at least one processor or central processing unit (CPU). TheCPUs are interconnected via system bus to various devices such as arandom access memory (RAM), read-only memory (ROM), and an input/output(I/O) adapter. The I/O adapter can connect to peripheral devices, suchas disk units and tape drives, or other program storage devices that arereadable by the system. The system can read the inventive instructionson the program storage devices and follow these instructions to executethe methodology of the embodiments herein.

The system further includes a user interface adapter that connects akeyboard, mouse, speaker, microphone, and/or other user interlacedevices such as a touch screen device (not shown) to the bus to gatheruser input. Additionally, a communication adapter connects the bus to adata processing network, and a display adapter connects the bus to adisplay device which may be embodied as an output device such as amonitor, printer, or transmitter, for example.

The preceding description has been presented with reference to variousembodiments. Persons having ordinary skill in the art and technology towhich this application pertains will appreciate that alterations andchanges in the described structures and methods of operation can bepracticed without meaningfully departing from the principle, spirit andscope.

What is claimed is:
 1. A processor implemented method comprising:obtaining, by a hardware processor in real time, an input stream datafrom one or more sources; filtering, by said hardware processor in saidreal time, said input stream data by identifying one or more authenticsources from said one or more sources to obtain filtered input streamdata; parsing, by said hardware processor in said real time, saidfiltered input stream data such that said filtered input stream data isvalidated to obtain validated data in a specific data format; applying,by said hardware processor in said real time, one or more metadatadriven logics and one or more predefined rules on said validated data insaid specific data format and said one or more authentic sources;performing, by said hardware processor in said real time, an analysis onsaid validated data in said specific data format and on correspondingsaid one or more authentic sources; and generating, by said hardwareprocessor in said real time, one or more real time events based on saidanalysis.
 2. The processor implemented method of claim 1, whereingenerating one or more real time events comprises performing acomparison of a specific timestamp from said input stream data with apredetermined threshold defined in said one or more predefined rules. 3.The processor implemented method of claim 1, wherein generating one ormore real time events comprises performing a comparison of a specificcount from said input stream data with a predetermined threshold definedin said one or more predefined rules.
 4. The processor implementedmethod of claim 1, further comprising communicating a notificationmessage comprising a command to said one or more authentic sources basedon said analysis.
 5. A system comprising: a memory storing instructions;one or more communication interfaces; one or more hardware processorscoupled to said memory using said one or more communication interfaces,wherein said one or more hardware processors are configured by saidinstructions to obtain, in real time, input stream data from one or moresources, filter, in said real time, said input stream data byidentifying one or more authentic sources from said one or more sourcesto obtain filtered input stream data, parse, in said real time, saidfiltered input stream data such that said filtered input stream data isvalidated to obtain validated data in a specific data format, apply, insaid real time, one or more metadata driven logics and one or morepredefined rules on validated data in said specific data format and saidone or more authentic sources, perform, in said real time, an analysison said validated data in said specific data format and on correspondingsaid one or more authentic sources, and generate, in said real time, oneor more real time events based on said analysis.
 6. The system of claim5, wherein said one or more real time events are generated by performinga comparison of a specific timestamp from said input stream data with apredetermined threshold defined in said one or more predefined rules. 7.The system of claim 5, wherein said one or more real time events aregenerated by performing a comparison of a specific count from said inputstream data with a predetermined threshold defined in said one or morepredefined rules.
 8. The system of claim 5, wherein said one or morehardware processors are further configured by said instructions tocommunicate a notification message comprising a command to said one ormore authentic sources based on said analysis.
 9. One or morenon-transitory machine readable information storage mediums comprisingone or more instructions which when executed by one or more hardwareprocessors causes: obtaining, by a hardware processor in real time, aninput stream data from one or more sources; filtering, by said hardwareprocessor in said real time, said input stream data by identifying oneor more authentic sources from said one or more sources to obtainfiltered input stream data; parsing, by said hardware processor in saidreal time, said filtered input stream data such that said filtered inputstream data is validated to obtain validated data in a specific dataformat; applying, by said hardware processor in said real time, one ormore metadata driven logics and one or more predefined rules on saidvalidated data in said specific data format and said one or moreauthentic sources; performing, by said hardware processor in said realtime, an analysis on said validated data in said specific data formatand on corresponding said one or more authentic sources; and generating,by said hardware processor in said real time, one or more real timeevents based on said analysis.
 10. The one or more non-transitorymachine readable information storage mediums of claim 9, whereingenerating one or more real time events comprises performing acomparison of a specific timestamp from said input stream data with apredetermined threshold defined in said one or more predefined rules.11. The one or more non-transitory machine readable information storagemediums of claim 9, wherein generating one or more real time eventscomprises performing a comparison of a specific count from said inputstream data with a predetermined threshold defined in said one or morepredefined rules.
 12. The one or more non-transitory machine readableinformation storage mediums of claim 9, further comprising communicatinga notification message comprising a command to said one or moreauthentic sources based on said analysis.